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A new method to assess land surface fractional open water ( fw ) inundation dynamics was developed using high frequency (89 GHz) brightness temperatures ( T b ) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with other ancillary inputs from AMSR-E and MODIS (Moderate Resolution Imaging Spectroradiometer). The resulting 89 GHz fw ( fw WBand ) retrievals provide 10-day temporal fidelity and enhanced (5 km) resolution relative to other passive microwave sensor based land observations. A look-up table was first established to provide reference microwave emissivities for water and land endmembers under a range of atmosphere and land surface conditions. The fw WBand retrievals were then obtained on a per pixel basis using difference ratio and double difference methods for respective barren and vegetated soil conditions. The resulting fw WBand summer (JJA) seasonal composites correspond favorably ( R ≥ 0.86, p < 0.001) with alternative, finer resolution (≤ 300 m) static open water maps derived from satellite radar and optical-infrared sensors over a pan-Arctic (≥ 45°N) domain. The fw WBand retrievals also capture significant seasonal drought and flooding events inferred from river discharge records within the major Arctic basins. The fw WBand retrievals are found to be reliable for discriminating open water in moderately dense vegetation areas, but with greater uncertainty over barren land. The ability of the fw retrievals to detect surface water under vegetation also decreases exponentially with increasing canopy optical thickness. The new fw WBand dataset provides an improved resolution satellite environmental data record documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change. [ABSTRACT FROM AUTHOR]

Abstract: Continuous ground-based total ozone measurements were carried out at Ny-Ålesund (78.9°N, 11.9° E, Svalbard islands, Norway), by using a Brewer spectrophotometer, for almost three years (2007–2009). Given the scarcity of high-quality ground-based measurements performed at high latitudes, this dataset provides a rare opportunity to check the quality of satellite-based data recorded in Arctic. Considering the unprecedented Artic ozone loss recently derived from satellite observations, assessing the reliability of satellite readings at such high latitudes is a timely effort. This study focuses on the comparisons between ground-based Brewer total ozone measurements and satellite ozone readings retrieved from EOS Aura Ozone Measurement Instrument (OMI) (by using OMI-TOMS and OMI-DOAS algorithms), retrieved from ERS-2 Global Ozone Monitoring Experiment (GOME) (by using TOGOMI algorithm) and from Envisat SCanning Imaging Absorption spectrometer for Atmospheric CartographY (SCIAMACHY) (by using TOSOMI algorithm). Despite the extreme conditions that affect both ground- and satellite-based measurements, a good agreement was found (r =0.99 for OMI-TOMS, r =0.97 for both OMI-DOAS and GOME datasets and r =0.96 for SCIAMACHY). Nevertheless, space-based total ozone readings were found to underestimate ground-based data, in agreement with prior results. OMI-TOMS data showed the highest negative mean bias (MB=−2.03%) whereas DOAS-based datasets showed a better agreement (MB=−0.47, −0.69 and −0.01% for OMI-DOAS, GOME and SCIAMACHY, respectively) but a greater data scatter. On the other hand, DOAS-based datasets present an important seasonal dependence; an overestimation of the Brewer total ozone up to about 3% is found in April and September with respect to OMI-DOAS and SCIAMACHY while in September with respect to GOME. In contrast, differences between OMI-TOMS and Brewer total ozone do not present significant dependence on the season or on geometrical parameters (i.e., satellite solar and viewing zenith angles). Differences in total ozone values between ground-based and satellite measurements are further discussed attending to the algorithm differences in dealing with cloud cover under high surface albedo (i.e., snow) conditions. Extending the study to Sodankyla station (67.3°N, 26.6°E, Finland), similarities and differences between the two stations have been highlighted. The agreement between satellite and Brewer data slightly improved when Sodankyla station is considered. On the continent, the seasonal dependence and data scatter for DOAS-based datasets and the MB for OMI-TOMS are smaller with respect to Ny-Ålesund. Moreover, the roughly constant ozone summer offset (about 1%) between OMI-TOMS and OMI-DOAS, well evident for Ny-Ålesund, is not present for Sodankyla. Finally, some patterns in the geographic distributions of the satellite-Brewer total ozone differences are evident only for Ny-Ålesund, partly because of the peculiar characteristics of the surface albedo (higher than in Sodankyla, with a strong water/land contrast). [Copyright &y& Elsevier]

Spatial and temporal distributions of snow grain size, mass fraction of snow impurity, and snow surface temperature around the northern polar region were retrieved together with snow and sea ice cover extents from radiance data observed by the Global Imager (GLI), an optical sensor onboard Japan''s Advanced Earth Observing Satellite-II (ADEOS-II). The sensor acquired global radiance data from April 2 to October 24 in 2003. Two grain sizes, one for the shallow (0–20 cm) layer and the other for the top surface (0–2 cm) layer, were retrieved using channels with different light penetration depths. Snow and sea ice related products derived from the Advanced Microwave Scanning Radiometer (AMSR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for comparison. Retrieved seasonal variations of snow and sea ice cover extents were consistent with the MODIS and AMSR products. GLI derived snow surface temperatures were also correlated with the MODIS surface temperatures and exhibited latitudinal dependence over the northern hemisphere. Retrieved snow impurities were considered to be partly inaccurate over the Arctic sea ice in April and over inland areas of the continents possibly due to thin snow and/or dense vegetation. However, the snow cover around the northern Canadian Arctic tundra and Archipelago regions and the Greenland ice sheet in April to June was found to be very clean with a soot-equivalent impurity fraction of around 0.05 ppmw or less. Thus, these areas can be used as reference sites for monitoring pollution levels of the Arctic snow cover. Retrieved snow grain radii in the shallow layer seemed erroneous over thin snow covers, but indicated drastic spatial and seasonal evolution of the snow cover over the northern hemisphere with radius ranging from 50 μm to over 1000 μm synchronized with variations in surface temperature. The spatial variability of the top surface grain radius was partly different from that of the grain size in the shallow layer suggesting the possibility of detecting vertical inhomogeneity of the top 10 cm of the snow cover. [Copyright &y& Elsevier]

Expert labels were used to evaluate arctic cloud detection accuracies of several methods based on MISR angular radiances and MODIS spectral radiances. The accuracy of cloud detections was evaluated relative to 5. 086 million expert labels applied to 7. 114 million 1. 1-km resolution pixels with valid radiances from 57 scenes. The accuracy of the MODIS operational cloud mask was 90. 72% for the 32 partly cloudy scenes and 93. 37% for the 25 completely clear and overcast scenes. An automated, simple threshold algorithm based on three features extracted from MISR radiances and the MODIS operational cloud mask agreed with each other for 74. 91% of the pixels in the 32 partly cloudy scenes and 78. 44% of the pixels in the 25 completely clear and overcast scenes. These subsets of pixels had, relative to the expert labels, classification accuracies of 96. 53% for the 32 partly cloudy scenes and 99. 05% for the 25 completely clear and overcast scenes. Fisher''s quadratic discriminate analysis (QDA) trained on expert labels from the 32 partly cloud scenes was applied to MISR radiances, three features based on MISR radiances, MODIS radiances, and the six features of the MODIS operational cloud mask with accuracies ranging from 87. 51% to 96. 43%. Accuracies increased to about 97% when QDA with expert labels was applied to combined radiances or features from both MISR and MODIS. Operational QDA-based classifiers were developed using as training labels those pixels for which the MISR automated, simple threshold and MODIS operational cloud mask algorithms agreed. Training the QDA classifier on these automatic labels using MISR radiances, three features based on MISR radiances, MODIS radiances, and the six features of the MODIS operational cloud mask led to accuracies ranging from 85. 23% to 93. 62% for the 32 partly cloudy scenes. Classification accuracies increased to 93. 74% (93. 40%) when combined MISR and MODIS radiances (features) were used. The highest accuracy attained with any operational algorithm tested on all 57 scenes was 94. 51%. These results suggest that both MISR and MODIS radiances are sufficient for cloud detection in daytime polar regions. [Copyright &y& Elsevier]

The timing of spring river-ice breakup, a major annual event for physical, biological, and human systems on Arctic rivers, has been used to infer regional climate variations over the past century or more. Most observations of ice breakup are recorded as point data taken from selected ground-based stations. It is unknown whether these point observations are fully representative of breakup patterns elsewhere along the course of a river. Here, daily time series of moderate resolution imaging spectroradiometer (MODIS) and advanced very high resolution radiometer (AVHRR) satellite images are used to remotely sense spatial and temporal patterns in ice breakup along 1600–3300 km lengths of the Lena, Ob'', Yenisey, and Mackenzie Rivers. The first day of predominantly ice-free water is visually identified and mapped for ten years (1992–1993, 1995–1998, and 2000–2003), with a mean precision of ±1.75 days. The derived breakup dates show high correlation with ground-based observations, although a slight trend towards earlier satellite-derived dates can be traced to differences in the way ice breakup date is defined. Large ice jams are often observed, particularly at confluences, although smaller ice jams may not be visible due to the limited spatial resolution of the imagery used. At the watershed scale, spatial patterns in breakup seem to be primarily governed by latitude, timing of the spring flood wave, and location of confluences with major tributaries. Interestingly, channel-scale factors such as slope, width, and radius of curvature, which are known to influence ice breakup at the reach scale, do not appear to be major factors at the scale observed here. The degree of similarity between interannual trends in breakup date at distant points along a river is generally high, which supports the use of point-scale data to infer regional climate variations. This similarity does not hold true for the Mackenzie River, where substantial spatial differences in breakup trends are observed. A new variable, spatially integrated breakup date (di), uses weighted spatial averaging to provide a more encompassing measure of breakup timing. The Ob'' and Yenisey Rivers show similar trends in spatially integrated breakup date from year to year. In contrast, the Mackenzie and Lena show a remarkably consistent negative correlation, here attributed to sea surface temperature anomalies associated with the Pacific Decadal Oscillation Index. [Copyright &y& Elsevier]